Intelligent prognostics of machining tools based on adaptive variational mode decomposition and deep learning method with attention mechanism

Liu, Chongdang; Zhang, Linxuan; Niu, Jiahe; Ye, Rong; Wu, Cheng

doi:10.1016/j.neucom.2020.06.116

Cited by 56 publications

(11 citation statements)

References 34 publications

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“…The scores obtained with the ratios of 9:1, 8:2, 7:3, 6:4, 5:5 reach over 75. This is mainly because the proposed RUL prediction method based on multiple deep In order to further verify the effectiveness of the proposed RUL prediction method, it is compared with three traditional prediction methods SVR [32], ELM [33], and BPNN [34], and three latest RUL prediction methods VMD-CNN-LSTM [12], EDWT-CNN-LSTM [20], and MTW-CNN-BiLSTM [22]. The descriptions of different RUL prediction methods are presented in Table 10.…”

Section: ) Comparison With Different Signal Reconstruction Methodsmentioning

confidence: 99%

“…SVR [32] standard support vector regression algorithm ELM [33] standard extreme learning machines BPNN [34] standard back propagation neural network VMD-CNN-LSTM [12] VMD + 1D-convolution layer + batch normalization + max-pooling layer + attention based LSTM + desen layer EDWT-CNN-LSTM [20] EDWT + 1D-convolution layer + max-pooling layer + 2 LSTM layers + fully connected layer + sigmoid layer MTW-CNN-BiLSTM [22] MTW + 1D-convolution layer + max-pooling layer + 2 BiLSTM layers + 2 FNN layers Proposed the proposed CEEMDAN-IFTC-PSR-Ensemble CNN/BiLSTM 15 shows the comparison of prediction results of different RUL prediction methods. It also can be seen from Fig.…”

Section: Rul Prediction Methods Descriptionmentioning

confidence: 99%

“…Variational mode decomposition (VMD) can avoid the phenomenon of mode aliasing and has good anti-noise ability. Liu et al [12] proposed a novel RUL prediction approach for cutting tool in which the original vibration data are processed by VMD to get different components for feature extraction. However, VMD requires a huge effort to select the control parameters.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Remaining Useful Life Prediction Method Based on CEEMDAN-IFTC-PSR and Ensemble CNN/BiLSTM Model for Cutting Tool

Wan

Chen

et al. 2022

IEEE Access

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To accurately predict the remaining useful life (RUL) of cutting tool, a novel RUL prediction method is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose original cutting tool vibration signals to get six intrinsic mode function (IMF) components from each sample. Secondly, high-frequency IMF components and low-frequency IMF components are obtained from IMF components and they are respectively fused into high-frequency data and low-frequency data using the improved fine-to-coarse reconstruction (IFTC), and high-frequency data and low-frequency data are reconstructed using phase space reconstruction (PSR). Thirdly, multiple prediction branches are adopted to construct an ensemble RUL prediction model for cutting tool, the highfrequency data and low-frequency data are input into bi-directional long short-term memory (BiLSTM) and convolutional neural network (CNN) to train a RUL prediction model respectively in each prediction branch. Finally, a series of experiments are conducted to verify the effectiveness of the proposed RUL prediction method, and the results show that the proposed method obtains a high score of RUL prediction for cutting tool.

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Section: ) Comparison With Different Signal Reconstruction Methodsmentioning

confidence: 99%

Section: Rul Prediction Methods Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Remaining Useful Life Prediction Method Based on CEEMDAN-IFTC-PSR and Ensemble CNN/BiLSTM Model for Cutting Tool

Wan

Chen

et al. 2022

IEEE Access

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“…The dominant thought is to measure the similarity between the Key and the Query ( Mercer and Neufeld, 2021 ). Attention mechanism has been applied in speech NLP, image and other fields ( Luo et al, 2018 ; Yuan et al, 2018 ; Li et al, 2020a ; Liu et al, 2020 ), since it can select the most informative features of an input, adaptively consider the importance of a single feature and allow the model to make a more accurate judgment. As a special, self-attention ( Shaw et al, 2018 ; Hou et al, 2019 ), which calculates the response at a position in the sequence by attending to all positions within the same sequence has achieved notable success in modeling complicated relations ( Gao et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Sun

Cheng²,

Meng³

et al. 2023

Front. Genet.

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Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has been applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through a deep learning model, which did not consider the distribution differently of omics data. Moreover, these methods ignore the relationship of samples. To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample’s relationship for learning an accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition.

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“…It avoids mode aliasing problem and has obvious advantages in computation efficiency, due to the reason of non-recursive variational decomposition. VMD has been widely used in the field of feature extraction [22][23].…”

Section: Introductionmentioning

confidence: 99%

A novel in-situ tool wear monitoring approach using multivariate signal processing and intrinsic multiscale entropy analysis

Xu¹,

Yuan

Lv³

et al. 2023

Preprint

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Tool wear significantly affects the interface condition between the machining tool and the workpiece, causing nonlinear vibrations that negatively impact machining quality. The vibration on the axes of X, Y and Z are both generated during machining process, and multivariate vibration signals collected by triaxial accelerometers contain dynamical information of tool wear accurately and comprehensively. This paper proposes a novel in-situ tool wear monitoring approach using multivariate signal processing and intrinsic multiscale entropy analysis. Multivariate variational mode decomposition (MVMD) is firstly used to process multivariate vibration signals. The multivariate band-limited intrinsic mode functions (BLIMFs) contain nonlinear and nonstationary wear characteristics of multivariate vibration signals. Afterwards, the refined composite multiscale dispersion entropy (RCMDE) is employed to measure the complexity and regularity of multivariate BLIMFs quantitatively. Finally, the feature matrices composed of entropy values on multiple scale of multivariate BLIMFs are adopted as the input of CNN to achieve accurate tool wear monitoring. The results show the proposed approach is promising for tool wear monitoring.

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Intelligent prognostics of machining tools based on adaptive variational mode decomposition and deep learning method with attention mechanism

Cited by 56 publications

References 34 publications

A Novel Remaining Useful Life Prediction Method Based on CEEMDAN-IFTC-PSR and Ensemble CNN/BiLSTM Model for Cutting Tool

A Novel Remaining Useful Life Prediction Method Based on CEEMDAN-IFTC-PSR and Ensemble CNN/BiLSTM Model for Cutting Tool

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

A novel in-situ tool wear monitoring approach using multivariate signal processing and intrinsic multiscale entropy analysis

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